Hard Negative Mining The samples which satisfy the criteria used for training do not contribute to the loss and lead to smaller gradients and slower convergence. To combat this issue, several sampling and mining strategies [1]}, [2]}, [3]}, [4]}, [5]} have been developed, which use hard positives and negatives for training. Schroff et al. [5]} propose `semi-hard' triplet mining, where the negatives, which lie close to the anchor but farther than the positives, are selected. Harwood et al. [2]} present a smart mining-based method that adaptively selects the most informative samples. The drawback of such methods is that the trained models overfit on a subset of hard samples and underfit on the `easy' samples. As they rely on a subset of training samples, they fail to exploit the information other samples provide.
